Computer Sciences Commons^™

Using The Vast Challenge In Undergraduate Cs Research, Christopher P. Andrews, R. Jordan Crouser

Computer Science: Faculty Publications

The Visual Analytics Science and Technology (VAST) Challenge is a yearly competition designed to push forward visual analytics research through synthetic, yet realistic analytic tasks. In this paper, we discuss the challenges and the successes we have experienced incorporating the VAST Challenge and associated datasets into undergraduate research programs at two liberal arts colleges. We advocate for increased undergraduate participation in this and similar competitions, arguing they afford unique opportunities for positive development in early researchers.

Details Of Deformable Part Models For Automatically Georeferencing Historical Map Images, Nicholas Howe, Jerod Weinman, John Gouwar, Aabid Shamji

Computer Science: Faculty Publications

Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Us- ing an evaluation set of 20 historical maps …

Crowdsourcing Image Schemas, Dagmar Gromann, Jamie C. Macbeth

Computer Science: Faculty Publications

With their potential to map experiental structures from the sensorimotor to the abstract cognitive realm, image schemas are believed to provide an embodied grounding to our cognitive conceptual system, including natural language. Few empirical studies have evaluated humans’ intuitive understanding of image schemas or the coherence of image-schematic annotations of natural language. In this paper we present the results of a human-subjects study in which 100 participants annotate 12 simple English sentences with one or more image schemas. We find that human subjects recruited from a crowdsourcing platform can understand image schema descriptions and use them to perform annotations of …

Reports Of The Aaai 2019 Spring Symposium Series, Ioana Baldini, Clark Barrett, Antonio Chella, Carlos Cinelli, David Gamez, Leilani H. Gilpin, Knut Hinkelmann, Dylan Holmes, Takashi Kido, Murat Kocaoglu, William F. Lawless, Alessio Lomuscio, Jamie C. Macbeth, Andreas Martin, Ranjeev Mittu, Evan Patterson, Donald Sofge, Prasad Tadepalli, Keiki Takadama, Shomir Wilson

Computer Science: Faculty Publications

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have …

Linguistic Variation And Anomalies In Comparisons Of Human And Machine-Generated Image Captions, Minyue Dai, Sandra Grandic, Jamie C. Macbeth

Computer Science: Faculty Publications

Describing the content of a visual image is a fundamental ability of human vision and language systems. Over the past several years, researchers have published on major improvements on image captioning, largely due to the development of deep learning systems trained on large data sets of images and human-written captions. However, these systems have major limitations, and their development has been narrowly focused on improving scores on relatively simple “bag-of-words” metrics. Very little work has examined the overall complex patterns of the language produced by image-captioning systems and how it compares to captions written by humans. In this paper, we …

How Does Customer Service Offshoring Impact Customer Satisfaction?, Jonathan W. Whitaker, M. S. Krishnan, Claes Fornell, Forrest Morgeson

Management Faculty Publications

Information technology (IT) plays a vital role in customer relationship management (CRM), because CRM processes include the collection and analysis of customer information, firms use technology tools to interact with customers, and IT created the conditions under which firms can offshore CRM processes. Customers have negative perceptions toward offshoring, which suggests that firms might be reluctant to offshore IT-enabled CRM processes. However, firms have significantly increased offshoring for CRM processes, presenting a conundrum. Why would firms increase offshoring for CRM processes if there could be a risk to customer satisfaction?

This paper helps to resolve the conundrum by studying the …

Chronic Disease Management: How It And Analytics Create Healthcare Value Through The Temporal Displacement Of Care, Steven M. Thompson, Jonathan W. Whitaker, Rajiv Kohli, Craig Jones

Management Faculty Publications

The treatment of chronic diseases consumes 86% of U.S. healthcare costs. While healthcare organizations have traditionally focused on treating the complications of chronic diseases, advances in information technology (IT) and analytics can help clinicians and patients manage and slow the progression of chronic diseases to result in higher quality of life for patients and lower healthcare costs.

We build on prior research to introduce the notion of temporal displacement of care (TDC), in which IT and analytics create healthcare value by displacing the time at which providers and patients make interventions to improve healthcare outcomes and reduce costs. We propose …

Extensions To The Ontology Design Pattern Representation Language, Quinn Hirt, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Recently, modular ontology modeling has become a more popular ontology engineering paradigm. With it, the need for additional metadata associated with ontology design patterns has grown. The Ontology Pattern Language (OPLa) was developed to facilitate annotating ontologies with useful metadata, as well as supporting tooling infrastructure. In this paper, we detail three extensions to OPLa into a reorganized namespace: OPLa-core, containing the original annotations; OPLa-SD, for use in detailing schema diagrams; and OPLa-CP, an adaptation of the content-pattern annotation schema.

Panel: Broadening The Discussion Of Ethics In The Interaction Design And Children Community, Christopher Frauenberger, Monica Landoni, Jerry Alan Fails, Janet C. Read, Alissa N. Antle, Pauline Gourlet

Computer Science Faculty Publications and Presentations

Interaction Design and Children (IDC) as an academic field, and as a community, has a responsibility to engage with the many and diverse ethical challenges that arise from work that concerns the creation of digital technology for and with children – both in terms of research and industry contexts. This panel builds on a short history of similar events at previous conferences and aims to foster and strengthen the debate about ethical conduct and moral responsibilities in IDC. In this year’s panel, we seek to broaden the discussion by collecting ethical concerns, issues or dilemmas from within the community to …

The Seven Layers Of Complexity Of Recommender Systems For Children In Educational Contexts, Emiliana Murgia, Monica Landoni, Theo Huibers, Jerry Alan Fails, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Recommender systems (RS) in their majority focus on an average target user: adults. We argue that for non-traditional populations in specific contexts, the task is not as straightforward–we must look beyond existing recommendation algorithms, premises for interface design, and standard evaluation metrics and frameworks. We explore the complexity of RS in an educational context for which young children are the target audience. The aim of this position paper is to spell out, label, and organize the specific layers of complexity observed in this context.

Comodide – The Comprehensive Modular Ontology Engineering Ide, Cogan Shimizu, Karl Hammar

Computer Science and Engineering Faculty Publications

No abstract provided.

Modl: A Modular Ontology Design Library, Cogan Shimizu, Quinn Hirt, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Pattern-based, modular ontologies have several beneficial properties that lend themselves to FAIR data practices, especially as it pertains to Interoperability and Reusability. However, developing such ontologies has a high upfront cost, e.g. reusing a pattern is predicated upon being aware of its existence in the first place. Thus, to help overcome these barriers, we have developed MODL: a modular ontology design library. MODL is a curated collection of well-documented ontology design patterns, drawn from a wide variety of interdisciplinary use-cases. In this paper we present MODL as a useful resource for the development of high-quality, modular ontologies, discuss its use, …

Use Of Virtual Reality Technology In Medical Training And Patient Rehabilitation, Sankalp Mishra

Browse all Theses and Dissertations

Coaching patients to follow the rehabilitation routines correctly and timely after surgery is often a challenge due to the limited medical knowledge of patients and limited availability of clinicians. Similarly, it is also a challenge to train medical professionals with both the technical and communication skills required in their practices. The recent emergence of VR technologies shines the light on improving the current training practices. In this thesis research, I will look at the development and application of VR-based immersive training games for two particular cases: 1. Post hand surgery rehab; and, 2. Training for Social determinants of health (SDOH) …

A Psychosocial Behavioral Attribution Model: Examining The Relationship Between The “Dark Triad” And Cyber-Criminal Behaviors Impacting Social Networking Sites, Kim Withers

CCAC Theses and Dissertations

This study proposes that individual personality characteristics and behavioral triggering effects come together to motivate online victimization. It draws from psychology’s current understanding of personality traits, attribution theory, and criminological research. This study combines the current computer deviancy and hacker taxonomies with that of the Dark Triad model of personality mapping. Each computer deviant behavior is identified by its distinct dimensions of cyber-criminal behavior (e.g., unethical hacking, cyberbullying, cyberstalking, and identity theft) and analyzed against the Dark Triad personality factors (i.e., narcissism, Machiavellianism, and psychopathy). The goal of this study is to explore whether there are significant relationships among the …

Adaptable Privacy-Preserving Model, Emily Elizabeth Brown

CCAC Theses and Dissertations

Current data privacy-preservation models lack the ability to aid data decision makers in processing datasets for publication. The proposed algorithm allows data processors to simply provide a dataset and state their criteria to recommend an xk-anonymity approach. Additionally, the algorithm can be tailored to a preference and gives the precision range and maximum data loss associated with the recommended approach. This dissertation report outlined the research’s goal, what barriers were overcome, and the limitations of the work’s scope. It highlighted the results from each experiment conducted and how it influenced the creation of the end adaptable algorithm. The xk-anonymity model …

A Comprehensive Cybersecurity Defense Framework For Large Organizations, Willarvis Smith

CCAC Theses and Dissertations

There is a growing need to understand and identify overarching organizational requirements for cybersecurity defense in large organizations. Applying proper cybersecurity defense will ensure that the right capabilities are fielded at the right locations to safeguard critical assets while minimizing duplication of effort and taking advantage of efficiencies. Exercising cybersecurity defense without an understanding of comprehensive foundational requirements instills an ad hoc and in many cases conservative approach to network security. Organizations must be synchronized across federal and civil agencies to achieve adequate cybersecurity defense. Understanding what constitutes comprehensive cybersecurity defense will ensure organizations are better protected and more efficient. …

Adaptive Strategies Of Multi-Objective Optimization For Greener Networks, Hatem Yazbek

CCAC Theses and Dissertations

Increasing energy costs and environmental issues related to the Internet and wired networks continue to be a major concern. Energy-efficient or power-aware networks continue to gain interest in the research community. Existing energy reduction approaches do not fully address all aspects of the problem. We consider the problem of reducing energy by turning off network links, while achieving acceptable load balance, by adjusting link weights. Changing link weights frequently can cause network oscillation or instability in measuring the resulting traffic load, which is a situation to be avoided. In this research, we optimize two objectives, which are minimizing network power …

Improving Energy Consumption Of Java Programs, Mohit Kumar

Wayne State University Dissertations

Information and Communications Technologies (ICT) amounts for 10% of the world energy which will keep on growing in the future and 3% of the overall carbon footprint which is now more than the level of CO2 emission as that of the aviation industry. For many past years, the focus was on hardware to optimize the energy consumption of ICT systems. This includes dynamic adaptation of hardware techniques such as fine-grain clock gating, power gating, and dynamic voltage/frequency scaling. However, recent demands of exascale computation, as well as the increasing carbon footprint, require new breakthroughs to make ICT systems more energy-efficient. …

Security Analysis Of The Internet Of Things Using Digital Forensic And Penetration Testing Tools, Olajide Ojagbule

College of Graduate Studies: Theses & Dissertations

We exist in a universe where everything is related to the internet or each other like smart TVs, smart telephones, smart thermostat, cars and more. Internet of Things has become one of the most talked about technologies across the world and its applications range from the control of home appliances in a smart home to the control of machines on the production floor of an industry that requires less human intervention in performing basic daily tasks. Internet of Things has rapidly developed without adequate attention given to the security and privacy goals involved in its design and implementation. This document …

Anomaly Detection In Bacnet/Ip Managed Building Automation Systems, Matthew Peacock

Theses: Doctorates and Masters

Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet/IP security features are …

A Dynamic Fault Tolerance Model For Microservices Architecture, Hajar Hameed Addeen

Electronic Theses and Dissertations

Microservices architecture is popular for its distributive system styles due to the independent character of each of the services in the architecture. Microservices are built to be single and each service has its running process and interconnecting with a lightweight mechanism that called application programming interface (API). The interaction through microservices needs to communicate internally. Microservices are a service that is likely to become unreachable to its consumers because, in any distributed setup, communication will fail on occasions due to the number of messages passing between services. Failures can occur when the networks are unreliable, and thus the connections can …

Reducing The Large Class Code Smell By Applying Design Patterns, Bayan Turkistani

Electronic Theses and Dissertations

Software systems need continuous developing to cope and keep up with everchanging requirements. Source code quality affects the software development costs. In software refactoring object-oriented systems, Large Class, in particular, hinder the maintenance of a system by letting it difficult for software developers to understand and perform modifications. Also, it is making the development process labor-intensive and time-wasting. Reducing the Large Class code smell by applying design patterns can make the refactoring process more manageable, ease developing the system and decrease the effort required for the maintaining of software. To guarantee object-oriented software stays clear to read, understand and modify …

A Review Of Reasons For Failure In Applying Machine Learning To Financial Trading And An Experiment Investigating Combinatorial Purged Cross Validation’S Merit In Preventing The Most Prominent Of These Reasons, Multiple Testing Bias, Colin Fritz

Graduate Research Theses & Dissertations

The interest in applying machine learning to financial trading in the hedge fund industry has exploded in the last five years due to the massive success of a handful of ‘quantitative’ investment firms like Renaissance Technologies who has pioneered the use of machine learning techniques in investment since the 1980s. The failure rate of such firms attempting to deploy financial machine learning strategies is very high. This thesis reviews many of the causes for failure such as harmful correlations between examples in the dataset, redundant observations, improper data sampling paradigm, and multiple testing bias. Of these, multiple testing bias is …

Elf3 Is An Antagonist Of Oncogenic-Signalling-Induced Expression Of Emt-Tf Zeb1, D Liu, Y Skomorovska, J Song, E Bowler, R Harris, R Ravasz, S Bai, Marzieh Ayati, K Tamai, Mehmet Koyuturk

Computer Science Faculty Publications

Background: Epithelial-to-mesenchymal transition (EMT) is a key step in the transformation of epithelial cells into migratory and invasive tumour cells. Intricate positive and negative regulatory processes regulate EMT. Many oncogenic signalling pathways can induce EMT, but the specific mechanisms of how this occurs, and how this process is controlled are not fully understood.

Methods: RNA-Seq analysis, computational analysis of protein networks and large-scale cancer genomics datasets were used to identify ELF3 as a negative regulator of the expression of EMT markers. Western blotting coupled to siRNA as well as analysis of tumour/normal colorectal cancer panels was used to …

The Relationship Between Housing Affordability And Demographic Factors: Case Study For The Atlanta Beltline, Chapman T. Lindstrom

College of Graduate Studies: Theses & Dissertations

Housing affordability has been a widely examined subject for populations residing in major metropolitan regions around the world. The relationship between housing affordability and the city’s demographics and its volume of urban development are important to take into consideration. In the past two decades there has been an increasing volume of literature detailing Atlanta Georgia’s large-scale redevelopment project, the Atlanta BeltLine (ABL), and its relationship with Atlanta’s Metropolitan population and housing affordability. The first objective of this paper is to study the relationship between housing affordability at two scales within the Atlanta Metropolitan Area (AMA) for both renters and homeowners. …

Determining Political Inclination In Tweets Using Transfer Learning, Mehtab Iqbal

College of Graduate Studies: Theses & Dissertations

Last few years have seen tremendous development in neural language modeling for transfer learning and downstream applications. In this research, I used Howard and Ruder’s Universal Language Model Fine Tuning (ULMFiT) pipeline to develop a classifier that can determine whether a tweet is politically left leaning or right leaning by likening the content to tweets posted by @TheDemocrats or @GOP accounts on Twitter. We achieved 87.7% accuracy in predicting political ideological inclination.

High Dimensional Outlier Detection, Omid Khormali

Graduate Student Theses, Dissertations, & Professional Papers

In statistics and data science, outliers are data points that differ greatly from other observations in a data set. They are important attributes of the data because they can dramatically influence patterns and relationships manifested by non-outliers. It is therefore very important to detect and adequately deal with outliers. Recently, a novel algorithm, the ROMA algorithm, has been proposed [11]. In this paper, we propose a modification of the ROMA algorithm that reduces its computational complexity from $O(n^2 m)$ to $O((n/(2^m-o(1)))^2 m)$ where $n$ is the number of data points and $m$ is the dimension of the space. And as …

“My Name Is My Password:” Understanding Children’S Authentication Practices, Dhanush Kumar Ratakonda, Tyler French, Jerry Alan Fails

Computer Science Faculty Publications and Presentations

Children continue to use technology at an increasing rate, more and more of which require authentication via usernames and passwords.We seek to understand how children ages 5-11 years old create and use their credentials. We investigate children’s username and password understanding and practices from the perspective of both children and adults within the context of three security categories: credential composition (e.g. length of password), performance (e.g. time to enter), and credential mechanisms (e.g; a pattern or characters). We conducted a semi-structured interview with 22 children and an online survey with 33 adult participants (parents and teachers) to determine their practices …

กลไกจุดสนใจแบบเน็ตเวิร์กละเอียดสำหรับการจำแนกประเภทของรูปภาพอาหาร, วศิณี นุชศิริ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในปัจจุบัน มีจำนวนรูปภาพอาหารมากมายที่ถูกอัพโหลดผ่านเครือข่ายสังคม โดยรูปภาพส่วนหนึ่งไม่ได้รับการระบุป้ายชื่ออาหาร การใช้แอปพลิเคชันสำหรับการจำแนกประเภทรูปภาพของอาหาร สามารถช่วยระบุป้ายชื่อ และจัดจำแนกประเภทของรูปภาพอาหารเหล่านั้นได้ ปัญหาของงานจำแนกประเภทของรูปภาพอาหาร จัดเป็นงานที่ค่อนข้างมีความซับซ้อน เนื่องจากจำนวนของประเภทอาหารมีมากกว่าหนึ่งร้อยประเภท และอาหารบางประเภทยังมีลักษณะที่แตกต่างกันเล็กน้อย ไม่ว่าจะเป็นประเภทของส่วนผสม หรือลักษณะการจัดวางจาน ซึ่งปัญหาเหล่านี้นำไปสู่งานที่เรียกว่า งานจำแนกประเภทรูปภาพแบบละเอียด (Fine-grained Image Classification) ในปัจจุบันแบบจำลองนิวรอลเน็ตเวิร์กแบบคอนโวลูชันเชิงเส้นคู่ (Bilinear Convolutional Neural Networks หรือ B-CNN) ถูกนำมาใช้ในการจำแนกประเภทของรูปภาพอาหาร เนื่องจากแบบจำลองนี้มีความแม่นยำในการจำแนกประเภทของรูปภาพสูง และสามารถสกัดลักษณะของรูปภาพออกมาอย่างหลากหลาย เพื่อโฟกัสรายละเอียดของอาหารในแต่ละประเภท แต่เนื่องจากคุณลักษณะของรูปภาพที่ถูกสกัดมานั้น บางลักษณะอาจจะไม่ได้มีความสำคัญต่อรูปภาพนั้น ๆ ด้วยเหตุผลดังกล่าว งานวิจัยนี้จึงได้นำเสนอกลไกจุดสนใจ (Attention Mechanism) มาสกัดลักษณะที่จำเพาะของรูปภาพอาหารในแต่ละประเภท อีกทั้งงานวิจัยนี้เลือกคอนโวลูชันเน็ตเวิร์กที่มีประสิทธิภาพในการจำแนกประเภทของรูปภาพดีกว่าคอนโวลูชันเน็ตเวิร์กแบบอื่น ๆ ในปัจจุบัน คือ อินเซ็บชันเวอร์ชันสาม และ อินเซ็บชันเรสเน็ตเวอร์ชันสอง (Inception-Resnet-v2 หรือ In-res-v2) มาเป็นตัวสกัดลักษณะของรูปภาพ โดยงานวิจัยนี้ได้ทำการทดลองกับชุดข้อมูลเชิงรูปภาพ จาก Wongnai ซึ่งเป็นแอปพลิเคชันสำหรับการอัปโหลดรูปภาพอาหาร โดยผลการทดลองพบว่าแบบจำลองที่ได้นำเสนอ มีประสิทธิภาพในการจำแนกประเภทของรูปภาพอาหารได้อย่างถูกต้องแม่นยำมากขึ้นเมื่อเปรียบเทียบกับแบบจำลองอื่น ๆ

ระบบการจัดการมูลค่าข้อมูลจากเกมสู่เกมด้วยบล็อกเชน, ชานน ยาคล้าย

Chulalongkorn University Theses and Dissertations (Chula ETD)

แม้ว่าในปัจจุบันบล็อกเชนจะถูกนำมาใช้ประโยชน์ในหลายอุตสาหกรรม แต่ในอุตสาหกรรมเกมนั้น บล็อกเชนไม่ได้ถูกนำไปใช้อย่างกว้างขวางมากนัก นอกจากนี้ แม้ว่าในอุสาหกรรมเกมจะมีผู้เล่นอยู่เป็นจำนวนมาก แต่ก็ยังไม่มีเกมหรือแพลตฟอร์มใดที่ให้สิทธิผู้เล่นในการเป็นเจ้าของสินทรัพย์หรือข้อมูลภายในเกมอย่างแท้จริง โดยแม้จะมีความพยายามในการระดมทุนเพื่อทำเกมหรือแพลตฟอร์มที่ให้ผู้เล่นได้มีโอกาสเป็นเจ้าของสินทรัพย์หรือข้อมูลภายในเกมอยู่บ้าง แต่ก็ยังคงอยู่ในขั้นตอนการทดลองที่ยังไม่เสร็จสมบูรณ์ และผู้เล่นยังต้องพึ่งพาระบบนิเวศน์ของแพลตฟอร์มนั้น ๆ อีกด้วย ในวิทยานิพนธ์ฉบับนี้ ผู้วิจัยจึงประสงค์ที่จะนำเสนอสถาปัตยกรรมกลางที่ทำให้ผู้เล่นเกมสามารถเป็นเจ้าของเวลาที่ตนเองใช้ภายในเกมได้โดยใช้บล็อกเชนสาธารณะ ทั้งผู้เล่นยังสามารถนำเวลาดังกล่าวไปใช้ในเกมอื่นได้ด้วย โดยใช้มาตราฐานโทเคนดิจิทัล ERC-20 บนอีเธอเรี่ยม นอกจากนี้ รูปแบบสถาปัตยกรรมที่นำเสนอดังกล่าวยังสามารถประยุกต์ใช้ได้กับทุกบล็อกเชนสาธารณะ และยังเป็นประโยชน์ต่อทุกองค์ประกอบของระบบนิเวศน์ อาทิเช่น ผู้เล่น บล็อกเชนโหนด และผู้พัฒนาเกม โดยผลการทดลองในงานวิทยานิพนธ์นี้ ยังแสดงว่าแนวความคิดดังกล่าวทำให้ผู้เล่นใช้เวลาในการเล่นเกมนานขึ้น และมีแนวโน้มที่จะอยากเล่นเกมใหม่ๆ ที่สามารถนำมูลค่าในเกมเดิมไปใช้ได้ แต่ทั้งนี้ยังมีปัจจัยหลายอย่างที่มีผล อาทิเช่น ประเภทของเกม การแลกเปลี่ยนค่าของเวลาภายในเกม เป็นต้น